Method for assisting in making a decision on biometric data

ABSTRACT

The present invention relates to a method for assisting a user in making a decision to compare biometric data of an individual with data from a database relating to a large number of individuals, and biometric data is acquired for an individual concerned, that this data is encoded, that the data items are compared in pairs with corresponding data from the database, that, for each comparison score the duplicate occurrence frequency/non-duplicate occurrence frequency ration is established, that the product of all the available ratios is calculated, that this product is standardized, that the standardized ratio is compared to a pre-set threshold, that the values greater than the pre-set threshold are kept and that this result is submitted to the user for him to validate it as appropriate.

CROSS—REFERENCE TO RELATED APPLICATIONS

The present Application is based on International Application No.PCT/EP2006/068028, filed on Nov. 2, 2006, which in turn corresponds toFrench Application No. 05/11353 filed on Nov. 8, 2005, and priority ishereby claimed under 35 USC §119 based on these applications. Each ofthese applications are hereby incorporated by reference in theirentirety into the present application.

FIELD OF THE INVENTION

The present invention relates to a method for assisting in making adecision to compare biometric data.

BACKGROUND OF THE INVENTION

To compare the biometric data relating to two individuals and determinewhether they are the same person (the term “duplicate” then applying) ordifferent people (non-duplicates), several digital data items can beavailable. These correspond, for example, to the comparison scores ofeach of their ten fingers. The present application is more particularlyinterested in the merging of the scores of these data items, in order tobest make the duplicate/non-duplicate decision. The usual comparisonperformance measurements are error ratios, namely:

The FAR (False Acceptance Rate), which is a “duplicate” classificationrate for data concerning individuals who are in reality different,

The FRR (False Rejection Rate) which is a “non-duplicate” classificationrate for the data in fact belonging to one and the same individual.

When a large number of different comparison scores have to be processed,for example those relating to the ten fingers of an individual, in orderfor a single decision to be made, these scores are merged. In this case,the merging operator is effective if, for a given FAR, it minimizes theFRR (or conversely, if for a given FRR, it minimizes the FAR).

To perform the merge, the geometric mean m of the comparison scores ofeach of the ten fingers is calculated. Using a simple comparison of mwith a threshold, the “duplicate” or “non-duplicate” decision is made.The threshold is determined by trial and error from measurements made ona sample of data. Such a known method does, however, have the followingdrawbacks:

It deals badly with the case where certain digital data is not available(for example, because it is not possible to acquire the image of theprints of certain fingers).

It is applied ineffectively to the scores supplied by certain comparisonoperators. For example, in the case of two operators, it imposes ahyperbola branch as the decision boundary, which does not always make itpossible to obtain an optimum solution.

It presupposes that the various comparison operators supply uniformscores, which is not, for example, the case if fingerprint scores are tobe merged with facial recognition scores.

SUMMARY OF THE INVENTION

One object of the present invention is a method for assisting a user inmaking a decision to compare biometric data of an individual with datafrom a database relating to a large number of individuals, in order toreduce the number of cases in the database, a method making it possibleto deal, with a more or less constant quality, with the cases where allthe necessary data is available, like the cases where some of the datais missing, this method offering a decision boundary which can beadapted to the comparison operators that are to be merged, in order toobtain the best possible results (for example, an FRR that is as low aspossible for a given FAR), this method also making it possible to mergenon-uniform comparison scores.

The inventive method is a method for assisting a user in making adecision to compare biometric data of an individual with data from adatabase relating to a large number of individuals, and it ischaracterized in that biometric data is acquired for an individualconcerned, that this data is encoded, that the data items are comparedin pairs with corresponding data from the database, that, for eachcomparison score, the duplicate occurrence frequency/non-duplicateoccurrence frequency ratio is established, that the product of all theavailable ratios is calculated, that this product is standardized, thatthe standardized ratio is compared to a pre-set threshold, that thevalues greater than the pre-set threshold are kept, and that this resultis submitted to the user for him to validate it, as appropriate.

According to another characteristic of the invention, afterthresholding, at least one other selection similar to the first isperformed in cascade, with another set of scores obtained in otherconditions for obtaining scores.

According to another characteristic of the invention, the threshold isdetermined by trial and error on samples from the database.

According to another characteristic of the invention, for n biometricdata items to be compared, a set of 2̂n transcoders is used tostandardize the product of the available ratios, these transcoders beingof the LUT type. Each of the 2̂n LUTs is associated with one, and onlyone, of the 2̂n possible subsets of the indices of the known scores.

According to another characteristic of the invention, the 2̂n LUTs areinitialized by performing measurements for only a limited number ofthem, namely those for which the subset of the indices of the knownscores is the cardinal 2 or less, the others being calculated.

Still other objects and advantages of the present invention will becomereadily apparent to those skilled in the art from the following detaileddescription, wherein the preferred embodiments of the invention areshown and described, simply by way of illustration of the best modecontemplated of carrying out the invention. As will be realized, theinvention is capable of other and different embodiments, and its severaldetails are capable of modifications in various obvious aspects, allwithout departing from the invention. Accordingly, the drawings anddescription thereof are to be regarded as illustrative in nature, andnot as restrictive.

BRIEF DESCRIPTION OF THE DRAWING

The present invention is illustrated by way of example, and not bylimitation, in the figures of the accompanying drawings, whereinelements having the same reference numeral designations represent likeelements throughout and wherein:

DETAILED DESCRIPTION OF THE INVENTION

To implement the inventive method, a biometric database is firstconstructed from data acquired conventionally, then digitized andencoded. This database contains, for each of the individuals n biometricdata items. Then, a large number of comparisons are performed (forexample, several thousand to several hundred thousand) on these dataitems, on the one hand between non-duplicates and on the other handbetween duplicates. The result of each comparison is a list s of nscores: s=(s1, s2, . . . , sn). ND denotes the non-duplicates class andD denotes the duplicates class. The observation probabilitiesfD(s_(i))=P(si/D) for the duplicates and fND(si)=P(si/ND) for thenon-duplicates are measured with a conventional method of estimatingdistributions, for example with a Gaussian core.

To merge the scores of these various data items, the overall problem ofcomparing n different biometric data items is broken down into 2^(n)sub-problems, according to the available scores (the fact that data fromthe n data items considered is available is not a bar to theimplementation of the inventive method). Each of these sub-problems isidentified by the subset I⊂[1,2, . . . n] of the indices i for which thescores are known.

For each of these sub-problems, the procedure is as follows. Thefollowing is defined:

${r_{I}(s)} = {\prod\limits_{i \in I}^{\;}{{{fD}( s_{i} )}/{{fND}( s_{i} )}}}$

The classification operator simply performs a thresholding on this ratior_(I)(s). The following decision rule is deduced from it: the successiveobservations are classified as D if r_(I)(s)>=R_(I) and as ND otherwise.However, rather than maintain 2^(n) thresholds R_(I) (one for eachpossible subset I), it is preferable to convert r_(I) to a value thatcan be used independently of I. For each sub-problem, a mapping tabler_(I)->FAR is therefore established, according to the function: x

P(rI≧x). This value is the final score on which the D or ND decisionwill be made. This mapping is established as follows. Assuming thatthere is independence of the variables s_(i), the relation can becalculated. It will be noted that the values fD(s_(i)) and fND(si) aresufficient to perform this calculation. Then, a readjustment is made ifthe assumption of independence is not statistically borne out asfollows:

-   -   if card(I)<=2 the relation is established by a measurement over        a large number of comparisons,    -   otherwise, for each of the sub-problems associated with the        pairs of indices I′={i,j } included in I, the deviation is        measured between the calculated relation and the measured        relation. From the relation calculated for r_(I)->FAR on the one        hand and from the average of these deviations on the other hand,        the relation r_(i)->FAR is determined

With reference to the single figure of the drawing, there now follows adescription of the process of merging data according to the presentinvention. To do this, it is assumed that there are n fingerprints (n=10in this case) of an individual and/or other biometric data of thisindividual. For each of these prints, a “matching” is performed(comparison of the prints of the individual concerned with those in adatabase, for example a “Hough matching”) and a comparison score isobtained each time. This score is, for example, an integer from therange [0,1000]. The various corresponding scores S_(i) are denoted S1,S2, . . . . Sn at the top of the figure. These scores are each presentedto the input of a converter, respectively LUT1, LUT2, . . . . LUTn.These converters are mapping tables stored in memories of the “Look-UpTable” type, and each supply, for each input score value, the ratior_(i) equal to fD(s_(i))/fND(s_(i)), as specified above. Furthermore,according to a characteristic of the invention, the values of r_(i) arecalculated according to a base 10 logarithmic scale. Thus, a circuit 1,connected to the respective outputs of all the converters LUT1 to LUTn,presents at its output the product P of all the r_(i), that is P=(r₁*r₂* . . . *r_(n)). This circuit 1 comprises a simple adder whichcalculates the sum of the logarithms of all the r_(i).

The circuit 1 is followed by a set 2 of 2^(n) transcoders (for nbiometric data items at the inputs S1 to Sn). These transcoders are alsoof the LUT type and they are responsible for transcoding P into acorresponding score value (also expressed in its base 10 logarithmicvalue), that is, as an FAR value. Furthermore, the transcoders of theset 2 perform an interpolation. This interpolation is a log10 scalelinear interpolation. It is necessary because the input values (theratios rI) do not belong to a finite set (they are floating numbers).

The scores available at the output of the set 2 in logarithmic form areconverted into linear values by a conversion circuit 3, then sent to athresholding circuit 4. This circuit 4 compares the FAR calculated inthis way by the circuits that precede it with a threshold thatrepresents the real FAR of the set. This threshold is adjusted so thatthe error rate taken into account for the subsequent processingoperations does not exceed an acceptable value (for example, for a humanoperator examining the output of the circuit 5, mentioned below, not tohave too many checks to be performed). The circuit 4 comprises twooutputs 5 and 6, respectively “duplicate” and “non-duplicate”. A signalappears on the output 5 when the FAR value from the circuit 3 is lessthan the threshold of the circuit 4, and on the output 6 otherwise. Itwill be noted that, in the case where a signal appears on the output 5,a human operator carries out additional checks of conventional type tovalidate only the responses that are estimated to be good.

It will be readily seen by one of ordinary skill in the art that thepresent invention fulfils all of the objects set forth above. Afterreading the foregoing specification, one of ordinary skill in the artwill be able to affect various changes, substitutions of equivalents andvarious aspects of the invention as broadly disclosed herein. It istherefore intended that the protection granted hereon be limited only bydefinition contained in the appended claims and equivalents thereof.

1. A method for assisting a user in making a decision to comparebiometric data of an individual with data from a database relating to alarge number of individuals, wherein biometric data is acquired for anindividual concerned, that this data is encoded, that the data items arecompared in pairs with corresponding data from the database, andwherein, for each comparison score, the duplicate occurrencefrequency/non-duplicate occurrence frequency ratio is established, theproduct of all the available ratios is calculated, this product isstandardized, the standardized ratio is compared to a pre-set threshold,the values greater than the pre-set threshold are kept, and this resultis submitted to the user for him to validate, it as appropriate.
 2. Themethod as claimed in claim 1, wherein, after thresholding, at least oneother selection similar to the first is performed in cascade, withanother set of scores obtained in other conditions for obtaining scores.3. The method as claimed in claim 1, wherein the product of theavailable ratios is calculated by adding their logarithms.
 4. The methodas claimed in claim 1, wherein the threshold is determined by trial anderror on samples from the database.
 5. The method as claimed in claim 1,wherein, for n biometric data items to be compared, a set of 2̂ntranscoders is used to standardized the product of the available ratios,these transcoders being of the LUT type.
 6. The method as claimed inclaim 5, wherein each of the 2̂n LUTs is associated with one, and onlyone, of the 2̂n possible subsets of the indices of a know scores.
 7. Themethod as claimed in claim 5, wherein 2̂n LUTS are initialized byperforming measurements for only a limited number of them, namely thosefor which the subset of the indices of the known scores is the cardinal2 or less, the others being calculated.
 8. A device for implementing themethod as claimed in claim 1, comprising, at each score value output ofa device for comparing biometric data, a device for converting scorevalues into “duplicate occurrence frequency/non-duplicate occurrencefrequency” ratios, the outputs of all the convert devices being linkedto a multiplier device followed by a set of 2̂n transcoders of productvalues into scores, a thresholding device with two outputs, “duplicate”and “non-duplicate”.
 9. The device as claimed in claim 8, wherein theconverter devices supply logarithmic values, that the multiplier devicecomprise an adder and that a logarithmic-linear conversion device ispositioned between the set of transcoders and the thresholding device.